Perform deep sentiment analysis and social listening across Twitter, Reddit, and Instagram to track brand reputation at scale.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install social-sentiment
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install social-sentiment using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Social Sentiment?
The Social Sentiment skill provides developers and brand managers with the ability to monitor live social conversations across 1.5 billion indexed posts. By leveraging Openclaw Skills, users can surface critical themes, flag viral complaints, and compare competitor performance using data-driven insights. It is designed to handle large-scale analysis, supporting operations that process between 1,000 and 70,000 posts, which are then exported for deep analysis using Python and pandas.
This skill is particularly valuable for organizations that need to move beyond simple keyword tracking into sophisticated opinion mining. It enables a technical approach to social listening by providing direct access to raw data via CSV exports, allowing for customized sentiment classification and engagement-weighted reporting that reflects the true state of public discourse.
Social Sentiment Use Cases
- Tracking brand reputation and public opinion trends over 30-day windows.
- Identifying and responding to potential PR crises by flagging high-engagement negative posts.
- Performing competitive analysis to see how product launches compare to industry rivals.
- Extracting customer pain points from Reddit and Instagram to inform product roadmaps.
How Social Sentiment Works
- Execute keyword-based queries across Twitter, Reddit, or Instagram to identify relevant posts.
- Poll the operation status using a unique operation ID to monitor the progress of data collection.
- Generate and download a bulk CSV export containing up to 64,000 rows of social media data.
- Process the exported data using Python and pandas to classify text as positive, negative, or neutral.
- Compile a final sentiment report that includes engagement-weighted scores and identifies viral themes.
Social Sentiment Setup
To get started with this skill within the Openclaw Skills environment, you must install the mcporter tool and configure your access credentials.
npm install -g mcporter
# Run the setup skill to authenticate
mcporter call xpoz-setup
# Verify your access status
mcporter call xpoz.checkAccessKeyStatus
Social Sentiment Data Schema & Taxonomy
The skill manages data through a structured pipeline that facilitates long-term trend tracking and automated reporting.
| Data Component | Format | Description |
|---|---|---|
| Search Results | JSON | Raw metadata returned from platform queries |
| Bulk Export | CSV | Large-scale dataset for Python/pandas analysis |
| Sentiment Score | Numeric (0-100) | Engagement-weighted score representing brand health |
| Local Storage | /data/social-sentiment/ |
Recommended directory for maintaining historical trend data |
Social Sentiment Advanced Features
- Bulk CSV export support for up to 64,000 rows per individual operation.
- Python-integrated analysis workflows for custom NLP and keyword-based classification.
- Engagement-weighted scoring logic to prioritize viral content over low-impact posts.
- Multi-platform aggregation across Reddit for long-form feedback and Twitter for real-time trends.
- Automated PR crisis detection by filtering for high-velocity negative sentiment keywords.
SKILL.md
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